The present invention is generally directed to automated measures for optimally profiling a particularly targeted population of individuals so as to enable highly reliable predictive modeling and highly tailored communications based thereon. More specifically, the subject system for psychographic profiling employs a statistical engine for profiling individuals based on a predetermined set of personal data acquired for the targeted population. The acquired data may include qualitative and quantitative information relating comprehensively to subjective indicia for attitudinal, behavioral, and preferential attributes of the individuals to supplement other more objective information that may be available (such as health/medical, pharmaceutical, and hospital claims history data).
The need for reliable predictive modeling to guide proper policy and planning is prevalent throughout virtually all identifiable sectors of modern day society. They include both public and private sectors, and run across the full spectrum of industries, markets, and business areas. While the particular demographics of the populations targeted will undoubtedly vary across enterprises—as will the nature of the parameters predicted, and the purposes of the endeavor—the need to capture and reduce the necessary personal information to meaningfully equip the predictive modeling required is a prevailing need, regardless of enterprise.
The health industry is but one example of the numerous applications for the present invention. The need for optimal patient profiling, and highly reliable predictive modeling of future behavior based thereon, is of particular importance in this industry historically marked by wide scale waste, poor management, and lack of cost control. Health care providers, insurers, and regulators alike have long struggled to predict responsive behaviors of patient populations with accuracy in the hopes of efficiently allocating vital resources, while actually improving the quality of care and resulting outcomes. Despite myriad of often very complex efforts, their attempts have largely failed in this regard.
Consequently, there remains a shared need in health care applications as well as others for a comprehensive tool which effectively captures and optimally reduces both subjective and objective information personal to individuals in a given population for use in predictive modeling to assess future outcomes. There remains a need for such tool, moreover, which captures and optimally factors the psychographic persuasion and/or tendencies of individuals within the given population. There remains a particular need for such tool in health care applications to conveniently and effectively enable health care providers, insurers, and regulators to improve quality of care and resulting outcomes by proactively identifying behaviors and medical conditions that adversely affect the patient, employer, health plan, or the like.